

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a__												=						get_tests_dir('''fixtures/test_sentencepiece.model''')

a__												=						{'target_lang': 'fi', 'source_lang': 'en'}
a__												=						'>>zh<<'
a__												=						'Helsinki-NLP/'

if is_torch_available():
			a__												=						'pt'
elif is_tf_available():
			a__												=						'tf'
else:
			a__												=						'jax'

@require_sentencepiece
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       MarianTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Optional[Any]             =       True


							def 							__lowercase							(     self			)       ->       str:
												super().setUp()
												_a      :       int   =						["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
												_a      :       Tuple   =						dict(zip(_a			,				range(len(_a			)			)			)			)
												_a      :       str   =						Path(self.tmpdirname			)
												save_json(_a			,				save_dir / VOCAB_FILES_NAMES['''vocab''']			)
												save_json(_a			,				save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']			)
												if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																	copyfile(_a			,				save_dir / VOCAB_FILES_NAMES['''source_spm''']			)
																	copyfile(_a			,				save_dir / VOCAB_FILES_NAMES['''target_spm''']			)

												_a      :       Any   =						MarianTokenizer.from_pretrained(self.tmpdirname			)
												tokenizer.save_pretrained(self.tmpdirname			)


							def 							__lowercase							(     self			,				**_a			)       ->       MarianTokenizer:
												return MarianTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       int:
												return (
												    "This is a test",
												    "This is a test",
												)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						"</s>"
												_a      :       Dict   =						0

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a			)			,				_a			)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a			)			,				_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Optional[int]   =						list(self.get_tokenizer().get_vocab().keys()			)

												self.assertEqual(vocab_keys[0]			,				'''</s>'''			)
												self.assertEqual(vocab_keys[1]			,				'''<unk>'''			)
												self.assertEqual(vocab_keys[-1]			,				'''<pad>'''			)
												self.assertEqual(len(_a			)			,				9			)


							def 							__lowercase							(     self			)       ->       List[str]:
												self.assertEqual(self.get_tokenizer().vocab_size			,				9			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Dict   =						MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""			)
												_a      :       Any   =						en_de_tokenizer(['''I am a small frog''']			,				return_tensors=_a			)
												self.assertIsInstance(_a			,				_a			)
												_a      :       Tuple   =						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
												self.assertListEqual(_a			,				batch.input_ids[0]			)

												_a      :       int   =						tempfile.mkdtemp()
												en_de_tokenizer.save_pretrained(_a			)
												_a      :       Any   =						[x.name for x in Path(_a			).glob('''*'''			)]
												self.assertIn('''source.spm'''			,				_a			)
												MarianTokenizer.from_pretrained(_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       List[Any]   =						self.get_tokenizer()

												_a      :       Tuple   =						tok(
												    ['''I am a small frog''' * 1_0_0_0, '''I am a small frog''']			,				padding=_a			,				truncation=_a			,				return_tensors=_a			)
												self.assertIsInstance(_a			,				_a			)
												self.assertEqual(batch.input_ids.shape			,				(2, 5_1_2)			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       List[Any]   =						self.get_tokenizer()
												_a      :       Dict   =						tok(['''I am a tiny frog''', '''I am a small frog''']			,				padding=_a			,				return_tensors=_a			)
												self.assertIsInstance(_a			,				_a			)
												self.assertEqual(batch_smaller.input_ids.shape			,				(2, 1_0)			)


							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												# fmt: off
												_a      :       List[Any]   =						{"input_ids": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=_a			,				model_name='''Helsinki-NLP/opus-mt-en-de'''			,				revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''			,				decode_kwargs={'''use_source_tokenizer''': True}			,				)




							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Tuple   =						MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''			)

												_a      :       Optional[Any]   =						"Tämä on testi"
												_a      :       Union[str, Any]   =						"This is a test"

												_a      :       Optional[Any]   =						[7_6, 7, 2_0_4_7, 2]
												_a      :       Any   =						[6_9, 1_2, 1_1, 9_4_0, 2]

												_a      :       Tuple   =						tokenizer(_a			).input_ids
												self.assertListEqual(_a			,				_a			)

												_a      :       Union[str, Any]   =						tokenizer(text_target=_a			).input_ids
												self.assertListEqual(_a			,				_a			)

												_a      :       str   =						tokenizer.decode(_a			,				skip_special_tokens=_a			)
												self.assertEqual(_a			,				_a			)





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()